Overview

Dataset statistics

Number of variables24
Number of observations1631
Missing cells1651
Missing cells (%)4.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory305.9 KiB
Average record size in memory192.1 B

Variable types

Numeric19
Categorical4
Boolean1

Alerts

Aneu_neck is highly overall correlated with Aneu_width and 12 other fieldsHigh correlation
Aneu_width is highly overall correlated with Aneu_neck and 15 other fieldsHigh correlation
Aneu_depth is highly overall correlated with Aneu_neck and 15 other fieldsHigh correlation
Aneu_height is highly overall correlated with Aneu_width and 14 other fieldsHigh correlation
Aneu_volume is highly overall correlated with Aneu_neck and 14 other fieldsHigh correlation
coil_length1 is highly overall correlated with Aneu_neck and 15 other fieldsHigh correlation
coil_size1 is highly overall correlated with Aneu_neck and 15 other fieldsHigh correlation
coil_length2 is highly overall correlated with Aneu_neck and 15 other fieldsHigh correlation
coil_size2 is highly overall correlated with Aneu_neck and 15 other fieldsHigh correlation
coil_length3 is highly overall correlated with Aneu_neck and 15 other fieldsHigh correlation
coil_size3 is highly overall correlated with Aneu_neck and 15 other fieldsHigh correlation
coil_length4 is highly overall correlated with Aneu_neck and 15 other fieldsHigh correlation
coil_size4 is highly overall correlated with Aneu_neck and 14 other fieldsHigh correlation
coil_length5 is highly overall correlated with Aneu_neck and 15 other fieldsHigh correlation
coil_size5 is highly overall correlated with Aneu_width and 13 other fieldsHigh correlation
coil_count is highly overall correlated with Aneu_neck and 15 other fieldsHigh correlation
Aneu_width_label is highly overall correlated with Aneu_width and 11 other fieldsHigh correlation
Aneu_neck has 35 (2.1%) missing valuesMissing
Aneu_depth has 52 (3.2%) missing valuesMissing
Adj_tech has 68 (4.2%) missing valuesMissing
VER has 314 (19.3%) missing valuesMissing
coil_length3 has 54 (3.3%) missing valuesMissing
coil_size3 has 54 (3.3%) missing valuesMissing
coil_length4 has 165 (10.1%) missing valuesMissing
coil_size4 has 165 (10.1%) missing valuesMissing
coil_length5 has 348 (21.3%) missing valuesMissing
coil_size5 has 349 (21.4%) missing valuesMissing
Aneu_volume has 52 (3.2%) zerosZeros

Reproduction

Analysis started2023-09-16 06:30:55.926573
Analysis finished2023-09-16 06:31:28.848494
Duration32.92 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

ID
Real number (ℝ)

Distinct1629
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1320.6849
Minimum1
Maximum2515
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-09-16T15:31:29.120395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile188
Q1703.5
median1362
Q31924.5
95-th percentile2387.5
Maximum2515
Range2514
Interquartile range (IQR)1221

Descriptive statistics

Standard deviation706.52066
Coefficient of variation (CV)0.53496537
Kurtosis-1.1716869
Mean1320.6849
Median Absolute Deviation (MAD)611
Skewness-0.10446541
Sum2154037
Variance499171.44
MonotonicityNot monotonic
2023-09-16T15:31:29.328215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2426 2
 
0.1%
1564 2
 
0.1%
1747 1
 
0.1%
1746 1
 
0.1%
1745 1
 
0.1%
1744 1
 
0.1%
1743 1
 
0.1%
1742 1
 
0.1%
1741 1
 
0.1%
1740 1
 
0.1%
Other values (1619) 1619
99.3%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
8 1
0.1%
11 1
0.1%
16 1
0.1%
17 1
0.1%
20 1
0.1%
27 1
0.1%
30 1
0.1%
ValueCountFrequency (%)
2515 1
0.1%
2514 1
0.1%
2513 1
0.1%
2512 1
0.1%
2511 1
0.1%
2510 1
0.1%
2509 1
0.1%
2506 1
0.1%
2504 1
0.1%
2503 1
0.1%

Sex
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
woman
1233 
man
398 

Length

Max length5
Median length5
Mean length4.5119559
Min length3

Characters and Unicode

Total characters7359
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwoman
2nd rowwoman
3rd rowwoman
4th rowwoman
5th rowwoman

Common Values

ValueCountFrequency (%)
woman 1233
75.6%
man 398
 
24.4%

Length

2023-09-16T15:31:29.523697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T15:31:29.629498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
woman 1233
75.6%
man 398
 
24.4%

Most occurring characters

ValueCountFrequency (%)
m 1631
22.2%
a 1631
22.2%
n 1631
22.2%
w 1233
16.8%
o 1233
16.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7359
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 1631
22.2%
a 1631
22.2%
n 1631
22.2%
w 1233
16.8%
o 1233
16.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 7359
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 1631
22.2%
a 1631
22.2%
n 1631
22.2%
w 1233
16.8%
o 1233
16.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7359
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 1631
22.2%
a 1631
22.2%
n 1631
22.2%
w 1233
16.8%
o 1233
16.8%

Age
Real number (ℝ)

Distinct64
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.067443
Minimum16
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-09-16T15:31:29.681127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile39
Q151
median60
Q368
95-th percentile76
Maximum88
Range72
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.535929
Coefficient of variation (CV)0.19530097
Kurtosis-0.29708293
Mean59.067443
Median Absolute Deviation (MAD)8
Skewness-0.33175231
Sum96339
Variance133.07766
MonotonicityNot monotonic
2023-09-16T15:31:29.746323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58 64
 
3.9%
66 63
 
3.9%
64 61
 
3.7%
65 57
 
3.5%
57 55
 
3.4%
62 55
 
3.4%
69 51
 
3.1%
67 51
 
3.1%
63 50
 
3.1%
73 49
 
3.0%
Other values (54) 1075
65.9%
ValueCountFrequency (%)
16 1
 
0.1%
19 1
 
0.1%
20 1
 
0.1%
26 1
 
0.1%
27 2
 
0.1%
28 3
0.2%
29 2
 
0.1%
30 1
 
0.1%
31 5
0.3%
32 3
0.2%
ValueCountFrequency (%)
88 1
 
0.1%
87 3
 
0.2%
85 2
 
0.1%
84 3
 
0.2%
82 4
 
0.2%
81 10
0.6%
80 7
0.4%
79 10
0.6%
78 14
0.9%
77 14
0.9%

Aneu_location
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
ICA
1083 
ACA
230 
MCA
145 
BA
127 
VA
 
46

Length

Max length3
Median length3
Mean length2.8939301
Min length2

Characters and Unicode

Total characters4720
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowICA
2nd rowICA
3rd rowICA
4th rowICA
5th rowMCA

Common Values

ValueCountFrequency (%)
ICA 1083
66.4%
ACA 230
 
14.1%
MCA 145
 
8.9%
BA 127
 
7.8%
VA 46
 
2.8%

Length

2023-09-16T15:31:29.811937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T15:31:29.872758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ica 1083
66.4%
aca 230
 
14.1%
mca 145
 
8.9%
ba 127
 
7.8%
va 46
 
2.8%

Most occurring characters

ValueCountFrequency (%)
A 1861
39.4%
C 1458
30.9%
I 1083
22.9%
M 145
 
3.1%
B 127
 
2.7%
V 46
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4720
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1861
39.4%
C 1458
30.9%
I 1083
22.9%
M 145
 
3.1%
B 127
 
2.7%
V 46
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4720
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1861
39.4%
C 1458
30.9%
I 1083
22.9%
M 145
 
3.1%
B 127
 
2.7%
V 46
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1861
39.4%
C 1458
30.9%
I 1083
22.9%
M 145
 
3.1%
B 127
 
2.7%
V 46
 
1.0%

Aneu_neck
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct126
Distinct (%)7.9%
Missing35
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean4.570131
Minimum1.3
Maximum22.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-09-16T15:31:29.935686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile2.5
Q13.5
median4.2
Q35.3
95-th percentile7.6
Maximum22.4
Range21.1
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.7348257
Coefficient of variation (CV)0.37960087
Kurtosis13.053058
Mean4.570131
Median Absolute Deviation (MAD)0.9
Skewness2.2927986
Sum7293.929
Variance3.0096202
MonotonicityNot monotonic
2023-09-16T15:31:30.004210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.6 63
 
3.9%
4 55
 
3.4%
4.2 54
 
3.3%
3.9 53
 
3.2%
4.1 52
 
3.2%
4.4 52
 
3.2%
3.4 50
 
3.1%
3.1 47
 
2.9%
3.5 47
 
2.9%
4.3 46
 
2.8%
Other values (116) 1077
66.0%
ValueCountFrequency (%)
1.3 1
 
0.1%
1.5 1
 
0.1%
1.6 3
 
0.2%
1.7 3
 
0.2%
1.8 7
0.4%
1.9 3
 
0.2%
2 11
0.7%
2.1 8
0.5%
2.2 10
0.6%
2.27 1
 
0.1%
ValueCountFrequency (%)
22.4 1
 
0.1%
19.3 1
 
0.1%
16.6 1
 
0.1%
13.5 1
 
0.1%
12.2 1
 
0.1%
12 1
 
0.1%
11.8 1
 
0.1%
11.3 1
 
0.1%
11 4
0.2%
10.9 1
 
0.1%

Aneu_width
Real number (ℝ)

Distinct164
Distinct (%)10.1%
Missing13
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean5.5564567
Minimum1.2
Maximum25.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-09-16T15:31:30.070776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile3
Q14
median5
Q36.3
95-th percentile10.1
Maximum25.7
Range24.5
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation2.529527
Coefficient of variation (CV)0.45524102
Kurtosis10.747461
Mean5.5564567
Median Absolute Deviation (MAD)1.1
Skewness2.577929
Sum8990.347
Variance6.398507
MonotonicityNot monotonic
2023-09-16T15:31:30.139823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.5 51
 
3.1%
4 50
 
3.1%
4.7 49
 
3.0%
4.3 48
 
2.9%
4.4 46
 
2.8%
4.1 45
 
2.8%
5 42
 
2.6%
4.9 42
 
2.6%
4.2 41
 
2.5%
5.8 39
 
2.4%
Other values (154) 1165
71.4%
ValueCountFrequency (%)
1.2 1
 
0.1%
1.3 1
 
0.1%
1.4 2
0.1%
1.5 1
 
0.1%
1.8 2
0.1%
2 3
0.2%
2.1 4
0.2%
2.2 4
0.2%
2.3 2
0.1%
2.35 1
 
0.1%
ValueCountFrequency (%)
25.7 1
 
0.1%
23 1
 
0.1%
20.9 1
 
0.1%
20 4
0.2%
19.8 1
 
0.1%
19.54 1
 
0.1%
19.37 1
 
0.1%
19 1
 
0.1%
17.9 1
 
0.1%
17.7 1
 
0.1%

Aneu_depth
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct184
Distinct (%)11.7%
Missing52
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean5.5820684
Minimum1.8
Maximum28.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-09-16T15:31:30.201377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile3.2
Q14.2
median5
Q36.3
95-th percentile9.8
Maximum28.5
Range26.7
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation2.3905232
Coefficient of variation (CV)0.42825043
Kurtosis14.275847
Mean5.5820684
Median Absolute Deviation (MAD)1
Skewness2.892419
Sum8814.086
Variance5.714601
MonotonicityNot monotonic
2023-09-16T15:31:30.268198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 65
 
4.0%
4.5 54
 
3.3%
5 53
 
3.2%
4.3 51
 
3.1%
4.2 48
 
2.9%
5.2 48
 
2.9%
4.9 47
 
2.9%
3.9 47
 
2.9%
4.7 46
 
2.8%
4.6 44
 
2.7%
Other values (174) 1076
66.0%
(Missing) 52
 
3.2%
ValueCountFrequency (%)
1.8 1
 
0.1%
1.9 2
 
0.1%
2 2
 
0.1%
2.1 3
0.2%
2.2 1
 
0.1%
2.3 3
0.2%
2.4 1
 
0.1%
2.5 4
0.2%
2.6 6
0.4%
2.7 6
0.4%
ValueCountFrequency (%)
28.5 1
0.1%
21.5 2
0.1%
21.3 1
0.1%
21.27 1
0.1%
20.85 1
0.1%
18 1
0.1%
17.6 1
0.1%
17.03 1
0.1%
17 2
0.1%
16.9 1
0.1%

Aneu_height
Real number (ℝ)

Distinct156
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7615708
Minimum1.9
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-09-16T15:31:30.334294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile3.2
Q14.2
median5.1
Q36.4
95-th percentile10.3
Maximum31
Range29.1
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation2.8657981
Coefficient of variation (CV)0.49739875
Kurtosis22.556416
Mean5.7615708
Median Absolute Deviation (MAD)1.1
Skewness3.7727043
Sum9397.122
Variance8.212799
MonotonicityNot monotonic
2023-09-16T15:31:30.397712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.4 62
 
3.8%
4.9 53
 
3.2%
4.6 48
 
2.9%
5.2 48
 
2.9%
4.1 48
 
2.9%
4 47
 
2.9%
3.8 44
 
2.7%
3.9 44
 
2.7%
5.1 43
 
2.6%
4.2 43
 
2.6%
Other values (146) 1151
70.6%
ValueCountFrequency (%)
1.9 4
 
0.2%
2 1
 
0.1%
2.1 3
 
0.2%
2.4 2
 
0.1%
2.5 7
0.4%
2.6 2
 
0.1%
2.7 8
0.5%
2.8 5
 
0.3%
2.9 6
 
0.4%
3 16
1.0%
ValueCountFrequency (%)
31 1
0.1%
30.8 1
0.1%
30 1
0.1%
29.53 1
0.1%
26.9 1
0.1%
25.9 1
0.1%
25.4 1
0.1%
25 1
0.1%
24 1
0.1%
23 2
0.1%

Aneu_volume
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1100
Distinct (%)67.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.32998
Minimum0
Maximum8823.5193
Zeros52
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-09-16T15:31:30.472233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.460043
Q135.296217
median62.3133
Q3127.35186
95-th percentile486.55661
Maximum8823.5193
Range8823.5193
Interquartile range (IQR)92.055642

Descriptive statistics

Standard deviation455.00211
Coefficient of variation (CV)2.9674699
Kurtosis179.17673
Mean153.32998
Median Absolute Deviation (MAD)32.90406
Skewness11.746468
Sum250081.2
Variance207026.92
MonotonicityNot monotonic
2023-09-16T15:31:30.617768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 52
 
3.2%
41.58616 7
 
0.4%
33.4724 7
 
0.4%
32.61413333 7
 
0.4%
41.51865 7
 
0.4%
40.64102 6
 
0.4%
47.6652 6
 
0.4%
61.46707 6
 
0.4%
21.79683333 6
 
0.4%
23.7384 6
 
0.4%
Other values (1090) 1521
93.3%
ValueCountFrequency (%)
0 52
3.2%
2.76948 1
 
0.1%
3.231583333 1
 
0.1%
3.40062 1
 
0.1%
3.977333333 1
 
0.1%
4.30808 1
 
0.1%
5.01144 2
 
0.1%
5.296133333 1
 
0.1%
5.8404 1
 
0.1%
6.018333333 1
 
0.1%
ValueCountFrequency (%)
8823.51932 1
0.1%
8385.36215 1
0.1%
5175.766667 1
0.1%
5002.628846 1
0.1%
4924.967017 1
0.1%
4635.2052 1
0.1%
2993.466667 1
0.1%
2852.267147 1
0.1%
2556.1013 1
0.1%
2491.066667 1
0.1%

Adj_tech
Categorical

Distinct4
Distinct (%)0.3%
Missing68
Missing (%)4.2%
Memory size12.9 KiB
Double cathe
558 
Stent assist
483 
BAT
272 
Simple
250 

Length

Max length12
Median length12
Mean length9.4740883
Min length3

Characters and Unicode

Total characters14808
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSimple
2nd rowSimple
3rd rowSimple
4th rowBAT
5th rowBAT

Common Values

ValueCountFrequency (%)
Double cathe 558
34.2%
Stent assist 483
29.6%
BAT 272
16.7%
Simple 250
15.3%
(Missing) 68
 
4.2%

Length

2023-09-16T15:31:30.679296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T15:31:30.734384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
double 558
21.4%
cathe 558
21.4%
stent 483
18.5%
assist 483
18.5%
bat 272
10.4%
simple 250
9.6%

Most occurring characters

ValueCountFrequency (%)
t 2007
13.6%
e 1849
12.5%
s 1449
 
9.8%
1041
 
7.0%
a 1041
 
7.0%
l 808
 
5.5%
i 733
 
5.0%
S 733
 
5.0%
D 558
 
3.8%
o 558
 
3.8%
Other values (10) 4031
27.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11660
78.7%
Uppercase Letter 2107
 
14.2%
Space Separator 1041
 
7.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 2007
17.2%
e 1849
15.9%
s 1449
12.4%
a 1041
8.9%
l 808
6.9%
i 733
 
6.3%
o 558
 
4.8%
h 558
 
4.8%
c 558
 
4.8%
b 558
 
4.8%
Other values (4) 1541
13.2%
Uppercase Letter
ValueCountFrequency (%)
S 733
34.8%
D 558
26.5%
B 272
 
12.9%
A 272
 
12.9%
T 272
 
12.9%
Space Separator
ValueCountFrequency (%)
1041
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13767
93.0%
Common 1041
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 2007
14.6%
e 1849
13.4%
s 1449
10.5%
a 1041
 
7.6%
l 808
 
5.9%
i 733
 
5.3%
S 733
 
5.3%
D 558
 
4.1%
o 558
 
4.1%
h 558
 
4.1%
Other values (9) 3473
25.2%
Common
ValueCountFrequency (%)
1041
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 2007
13.6%
e 1849
12.5%
s 1449
 
9.8%
1041
 
7.0%
a 1041
 
7.0%
l 808
 
5.5%
i 733
 
5.0%
S 733
 
5.0%
D 558
 
3.8%
o 558
 
3.8%
Other values (10) 4031
27.2%

Is_bleb
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
False
1307 
True
324 
ValueCountFrequency (%)
False 1307
80.1%
True 324
 
19.9%
2023-09-16T15:31:30.793194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

VER
Real number (ℝ)

Distinct266
Distinct (%)20.2%
Missing314
Missing (%)19.3%
Infinite0
Infinite (%)0.0%
Mean25.97629
Minimum5.9
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-09-16T15:31:30.851336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5.9
5-th percentile17.68
Q122.4
median25.7
Q328.8
95-th percentile35.52
Maximum60
Range54.1
Interquartile range (IQR)6.4

Descriptive statistics

Standard deviation5.6824488
Coefficient of variation (CV)0.21875521
Kurtosis2.4684543
Mean25.97629
Median Absolute Deviation (MAD)3.2
Skewness0.66326665
Sum34210.774
Variance32.290224
MonotonicityNot monotonic
2023-09-16T15:31:30.929758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 22
 
1.3%
24.6 20
 
1.2%
26.9 18
 
1.1%
21.8 17
 
1.0%
27.9 16
 
1.0%
23.6 15
 
0.9%
25.2 15
 
0.9%
26.4 15
 
0.9%
27.2 14
 
0.9%
24.3 14
 
0.9%
Other values (256) 1151
70.6%
(Missing) 314
 
19.3%
ValueCountFrequency (%)
5.9 1
0.1%
8.1 1
0.1%
8.4 1
0.1%
8.8 1
0.1%
9.8 1
0.1%
10.8 1
0.1%
11 1
0.1%
11.3 1
0.1%
11.7 1
0.1%
12.1 1
0.1%
ValueCountFrequency (%)
60 1
0.1%
52.9 1
0.1%
52.8 1
0.1%
48.7 2
0.1%
45.3 1
0.1%
44.8 1
0.1%
44.4 1
0.1%
44.3 1
0.1%
44.1 2
0.1%
43.9 1
0.1%

coil_length1
Real number (ℝ)

Distinct27
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.491355
Minimum2
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-09-16T15:31:31.069314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q18
median8
Q315
95-th percentile30
Maximum50
Range48
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.9968577
Coefficient of variation (CV)0.60888013
Kurtosis4.0958543
Mean11.491355
Median Absolute Deviation (MAD)2
Skewness1.8387127
Sum18742.4
Variance48.956017
MonotonicityNot monotonic
2023-09-16T15:31:31.127313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
8 441
27.0%
6 264
16.2%
10 231
14.2%
15 216
13.2%
20 144
 
8.8%
4 90
 
5.5%
30 83
 
5.1%
12 61
 
3.7%
7 18
 
1.1%
3 16
 
1.0%
Other values (17) 67
 
4.1%
ValueCountFrequency (%)
2 1
 
0.1%
3 16
 
1.0%
4 90
 
5.5%
6 264
16.2%
7 18
 
1.1%
7.5 1
 
0.1%
8 441
27.0%
9 14
 
0.9%
10 231
14.2%
11 2
 
0.1%
ValueCountFrequency (%)
50 4
 
0.2%
45 1
 
0.1%
40 8
 
0.5%
36 1
 
0.1%
33 1
 
0.1%
30 83
5.1%
25 14
 
0.9%
24 1
 
0.1%
21 1
 
0.1%
20 144
8.8%

coil_size1
Real number (ℝ)

Distinct22
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7930717
Minimum1.5
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-09-16T15:31:31.181640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile3
Q13
median4
Q35
95-th percentile9
Maximum24
Range22.5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.3906017
Coefficient of variation (CV)0.49876193
Kurtosis17.151665
Mean4.7930717
Median Absolute Deviation (MAD)1
Skewness3.3301077
Sum7817.5
Variance5.7149765
MonotonicityNot monotonic
2023-09-16T15:31:31.248709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
4 420
25.8%
3 344
21.1%
5 282
17.3%
6 177
10.9%
3.5 63
 
3.9%
4.5 62
 
3.8%
8 61
 
3.7%
7 61
 
3.7%
2.5 41
 
2.5%
2 31
 
1.9%
Other values (12) 89
 
5.5%
ValueCountFrequency (%)
1.5 3
 
0.2%
2 31
 
1.9%
2.5 41
 
2.5%
3 344
21.1%
3.5 63
 
3.9%
4 420
25.8%
4.5 62
 
3.8%
5 282
17.3%
6 177
10.9%
7 61
 
3.7%
ValueCountFrequency (%)
24 2
 
0.1%
22 1
 
0.1%
20 9
 
0.6%
18 2
 
0.1%
16 3
 
0.2%
14 6
 
0.4%
13 1
 
0.1%
12 10
 
0.6%
11 1
 
0.1%
10 29
1.8%

coil_length2
Real number (ℝ)

Distinct27
Distinct (%)1.7%
Missing11
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean8.3650617
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-09-16T15:31:31.306334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q310
95-th percentile20
Maximum50
Range49
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.3017191
Coefficient of variation (CV)0.75333802
Kurtosis8.3248711
Mean8.3650617
Median Absolute Deviation (MAD)2
Skewness2.5419998
Sum13551.4
Variance39.711663
MonotonicityNot monotonic
2023-09-16T15:31:31.363051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
4 353
21.6%
6 352
21.6%
8 316
19.4%
10 175
10.7%
3 109
 
6.7%
15 82
 
5.0%
20 69
 
4.2%
30 56
 
3.4%
12 39
 
2.4%
2 33
 
2.0%
Other values (17) 36
 
2.2%
(Missing) 11
 
0.7%
ValueCountFrequency (%)
1 3
 
0.2%
2 33
 
2.0%
2.5 1
 
0.1%
3 109
 
6.7%
3.5 1
 
0.1%
4 353
21.6%
5 2
 
0.1%
5.4 1
 
0.1%
6 352
21.6%
7 2
 
0.1%
ValueCountFrequency (%)
50 4
 
0.2%
40 2
 
0.1%
36 1
 
0.1%
30 56
3.4%
25 2
 
0.1%
24 1
 
0.1%
20 69
4.2%
17 1
 
0.1%
15 82
5.0%
13.6 1
 
0.1%

coil_size2
Real number (ℝ)

Distinct23
Distinct (%)1.4%
Missing10
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean3.8022825
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-09-16T15:31:31.418269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12.5
median3
Q34
95-th percentile8
Maximum30
Range29
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation2.3597554
Coefficient of variation (CV)0.62061547
Kurtosis29.482309
Mean3.8022825
Median Absolute Deviation (MAD)1
Skewness4.3461732
Sum6163.5
Variance5.5684454
MonotonicityNot monotonic
2023-09-16T15:31:31.472089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
3 480
29.4%
2 295
18.1%
4 283
17.4%
5 138
 
8.5%
2.5 114
 
7.0%
6 95
 
5.8%
3.5 50
 
3.1%
4.5 36
 
2.2%
8 34
 
2.1%
7 22
 
1.3%
Other values (13) 74
 
4.5%
ValueCountFrequency (%)
1 8
 
0.5%
1.5 17
 
1.0%
2 295
18.1%
2.5 114
 
7.0%
3 480
29.4%
3.5 50
 
3.1%
4 283
17.4%
4.5 36
 
2.2%
5 138
 
8.5%
6 95
 
5.8%
ValueCountFrequency (%)
30 1
 
0.1%
24 2
 
0.1%
22 2
 
0.1%
20 5
 
0.3%
18 2
 
0.1%
16 3
 
0.2%
14 3
 
0.2%
13 1
 
0.1%
12 5
 
0.3%
10 14
0.9%

coil_length3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)1.5%
Missing54
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean7.0701332
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-09-16T15:31:31.533120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q38
95-th percentile20
Maximum50
Range49
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.7801594
Coefficient of variation (CV)0.81754605
Kurtosis12.257727
Mean7.0701332
Median Absolute Deviation (MAD)2
Skewness2.9560735
Sum11149.6
Variance33.410243
MonotonicityNot monotonic
2023-09-16T15:31:31.587873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
4 432
26.5%
6 265
16.2%
8 237
14.5%
3 208
12.8%
10 130
 
8.0%
2 93
 
5.7%
15 80
 
4.9%
20 41
 
2.5%
30 37
 
2.3%
12 24
 
1.5%
Other values (13) 30
 
1.8%
(Missing) 54
 
3.3%
ValueCountFrequency (%)
1 7
 
0.4%
2 93
 
5.7%
2.5 1
 
0.1%
3 208
12.8%
3.5 2
 
0.1%
4 432
26.5%
4.5 1
 
0.1%
5 1
 
0.1%
6 265
16.2%
7.5 1
 
0.1%
ValueCountFrequency (%)
50 4
 
0.2%
40 1
 
0.1%
30 37
2.3%
25 1
 
0.1%
20 41
2.5%
15 80
4.9%
12.2 1
 
0.1%
12 24
 
1.5%
11.9 1
 
0.1%
11 2
 
0.1%

coil_size3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)1.5%
Missing54
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean3.2958148
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-09-16T15:31:31.640763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.5
Q12
median3
Q34
95-th percentile6
Maximum30
Range29
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2415595
Coefficient of variation (CV)0.68012301
Kurtosis38.349904
Mean3.2958148
Median Absolute Deviation (MAD)1
Skewness4.9781493
Sum5197.5
Variance5.0245891
MonotonicityNot monotonic
2023-09-16T15:31:31.690473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2 493
30.2%
3 382
23.4%
4 190
 
11.6%
2.5 131
 
8.0%
5 107
 
6.6%
1.5 61
 
3.7%
6 49
 
3.0%
3.5 43
 
2.6%
8 27
 
1.7%
1 25
 
1.5%
Other values (13) 69
 
4.2%
(Missing) 54
 
3.3%
ValueCountFrequency (%)
1 25
 
1.5%
1.5 61
 
3.7%
2 493
30.2%
2.5 131
 
8.0%
3 382
23.4%
3.5 43
 
2.6%
4 190
 
11.6%
4.5 24
 
1.5%
5 107
 
6.6%
6 49
 
3.0%
ValueCountFrequency (%)
30 1
 
0.1%
26 1
 
0.1%
24 1
 
0.1%
22 1
 
0.1%
20 4
0.2%
18 1
 
0.1%
16 4
0.2%
14 2
 
0.1%
12 5
0.3%
10 7
0.4%

coil_length4
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)1.3%
Missing165
Missing (%)10.1%
Infinite0
Infinite (%)0.0%
Mean6.2770805
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-09-16T15:31:31.742504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q38
95-th percentile15
Maximum50
Range49
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.3077566
Coefficient of variation (CV)0.84557727
Kurtosis14.693993
Mean6.2770805
Median Absolute Deviation (MAD)2
Skewness3.1105867
Sum9202.2
Variance28.17228
MonotonicityNot monotonic
2023-09-16T15:31:31.795012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
4 403
24.7%
3 232
14.2%
6 197
12.1%
8 190
11.6%
2 175
10.7%
10 92
 
5.6%
15 72
 
4.4%
20 35
 
2.1%
30 20
 
1.2%
12 18
 
1.1%
Other values (9) 32
 
2.0%
(Missing) 165
10.1%
ValueCountFrequency (%)
1 15
 
0.9%
2 175
10.7%
2.5 1
 
0.1%
3 232
14.2%
3.5 1
 
0.1%
4 403
24.7%
6 197
12.1%
7 1
 
0.1%
8 190
11.6%
9 8
 
0.5%
ValueCountFrequency (%)
50 3
 
0.2%
40 1
 
0.1%
30 20
 
1.2%
24 1
 
0.1%
20 35
 
2.1%
15 72
4.4%
12.2 1
 
0.1%
12 18
 
1.1%
10 92
5.6%
9 8
 
0.5%

coil_size4
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)1.6%
Missing165
Missing (%)10.1%
Infinite0
Infinite (%)0.0%
Mean2.9782401
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-09-16T15:31:31.850035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.5
Q12
median2.5
Q33
95-th percentile6
Maximum30
Range29
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.0614975
Coefficient of variation (CV)0.69218647
Kurtosis50.635416
Mean2.9782401
Median Absolute Deviation (MAD)0.5
Skewness5.6503005
Sum4366.1
Variance4.2497719
MonotonicityNot monotonic
2023-09-16T15:31:31.985088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2 596
36.5%
3 291
17.8%
4 145
 
8.9%
2.5 99
 
6.1%
1.5 98
 
6.0%
5 81
 
5.0%
6 35
 
2.1%
1 34
 
2.1%
3.5 24
 
1.5%
8 19
 
1.2%
Other values (14) 44
 
2.7%
(Missing) 165
 
10.1%
ValueCountFrequency (%)
1 34
 
2.1%
1.5 98
 
6.0%
2 596
36.5%
2.5 99
 
6.1%
3 291
17.8%
3.5 24
 
1.5%
3.6 1
 
0.1%
4 145
 
8.9%
4.5 12
 
0.7%
5 81
 
5.0%
ValueCountFrequency (%)
30 1
 
0.1%
26 1
 
0.1%
22 2
0.1%
20 2
0.1%
18 1
 
0.1%
16 1
 
0.1%
14 2
0.1%
12 4
0.2%
11 1
 
0.1%
10 3
0.2%

coil_length5
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)1.6%
Missing348
Missing (%)21.3%
Infinite0
Infinite (%)0.0%
Mean5.832424
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-09-16T15:31:32.043077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q38
95-th percentile15
Maximum50
Range49
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.1660396
Coefficient of variation (CV)0.88574486
Kurtosis16.876671
Mean5.832424
Median Absolute Deviation (MAD)2
Skewness3.4020069
Sum7483
Variance26.687965
MonotonicityNot monotonic
2023-09-16T15:31:32.098670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4 312
19.1%
3 228
14.0%
2 211
12.9%
6 191
11.7%
8 134
 
8.2%
10 87
 
5.3%
15 44
 
2.7%
20 23
 
1.4%
30 18
 
1.1%
1 13
 
0.8%
Other values (11) 22
 
1.3%
(Missing) 348
21.3%
ValueCountFrequency (%)
1 13
 
0.8%
2 211
12.9%
2.5 1
 
0.1%
3 228
14.0%
3.5 2
 
0.1%
4 312
19.1%
4.5 1
 
0.1%
6 191
11.7%
7 1
 
0.1%
8 134
8.2%
ValueCountFrequency (%)
50 2
 
0.1%
45 1
 
0.1%
33 1
 
0.1%
30 18
1.1%
21 1
 
0.1%
20 23
1.4%
17 1
 
0.1%
15 44
2.7%
12 10
 
0.6%
11 1
 
0.1%

coil_size5
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)1.6%
Missing349
Missing (%)21.4%
Infinite0
Infinite (%)0.0%
Mean2.8295632
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-09-16T15:31:32.148674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.5
Q12
median2
Q33
95-th percentile5
Maximum24
Range23
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.8849645
Coefficient of variation (CV)0.666168
Kurtosis39.155225
Mean2.8295632
Median Absolute Deviation (MAD)0.5
Skewness5.1150134
Sum3627.5
Variance3.553091
MonotonicityNot monotonic
2023-09-16T15:31:32.200183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 561
34.4%
3 246
15.1%
4 106
 
6.5%
1.5 96
 
5.9%
2.5 93
 
5.7%
5 62
 
3.8%
1 36
 
2.2%
6 25
 
1.5%
3.5 15
 
0.9%
8 9
 
0.6%
Other values (10) 33
 
2.0%
(Missing) 349
21.4%
ValueCountFrequency (%)
1 36
 
2.2%
1.5 96
 
5.9%
2 561
34.4%
2.5 93
 
5.7%
3 246
15.1%
3.5 15
 
0.9%
4 106
 
6.5%
4.5 7
 
0.4%
5 62
 
3.8%
6 25
 
1.5%
ValueCountFrequency (%)
24 1
 
0.1%
20 3
 
0.2%
18 2
 
0.1%
16 1
 
0.1%
14 2
 
0.1%
12 3
 
0.2%
10 5
0.3%
9 1
 
0.1%
8 9
0.6%
7 8
0.5%

coil_count
Real number (ℝ)

Distinct39
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9865113
Minimum1
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2023-09-16T15:31:32.258715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median7
Q310
95-th percentile17
Maximum60
Range59
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.3634587
Coefficient of variation (CV)0.67156465
Kurtosis20.275234
Mean7.9865113
Median Absolute Deviation (MAD)2
Skewness3.2840469
Sum13026
Variance28.766689
MonotonicityNot monotonic
2023-09-16T15:31:32.317699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
5 220
13.5%
6 220
13.5%
4 184
11.3%
7 174
10.7%
8 137
8.4%
9 117
7.2%
3 111
6.8%
10 81
 
5.0%
11 80
 
4.9%
12 49
 
3.0%
Other values (29) 258
15.8%
ValueCountFrequency (%)
1 10
 
0.6%
2 44
 
2.7%
3 111
6.8%
4 184
11.3%
5 220
13.5%
6 220
13.5%
7 174
10.7%
8 137
8.4%
9 117
7.2%
10 81
 
5.0%
ValueCountFrequency (%)
60 2
0.1%
54 1
0.1%
50 1
0.1%
49 1
0.1%
35 1
0.1%
34 1
0.1%
33 1
0.1%
32 2
0.1%
31 1
0.1%
30 2
0.1%

Aneu_width_label
Categorical

Distinct3
Distinct (%)0.2%
Missing13
Missing (%)0.8%
Memory size12.9 KiB
0.0
807 
1.0
513 
2.0
298 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4854
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
0.0 807
49.5%
1.0 513
31.5%
2.0 298
 
18.3%
(Missing) 13
 
0.8%

Length

2023-09-16T15:31:32.376232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T15:31:32.432369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 807
49.9%
1.0 513
31.7%
2.0 298
 
18.4%

Most occurring characters

ValueCountFrequency (%)
0 2425
50.0%
. 1618
33.3%
1 513
 
10.6%
2 298
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3236
66.7%
Other Punctuation 1618
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2425
74.9%
1 513
 
15.9%
2 298
 
9.2%
Other Punctuation
ValueCountFrequency (%)
. 1618
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4854
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2425
50.0%
. 1618
33.3%
1 513
 
10.6%
2 298
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4854
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2425
50.0%
. 1618
33.3%
1 513
 
10.6%
2 298
 
6.1%

Interactions

2023-09-16T15:31:24.031676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:30:56.607966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:30:57.850501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:30:59.069890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:00.307027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:01.559326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:02.787833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:03.951948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:05.339442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:07.069911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:08.474739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:10.380502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:13.570917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:14.995899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:16.534358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:18.293078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:19.712363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:21.167504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:22.635811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:24.198856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:30:56.668514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:30:57.911079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:30:59.213241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:00.369019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:01.618874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:02.863865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:04.010048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:05.401487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:07.147441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:08.540982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:10.484038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:13.666602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:15.058729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:16.614601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:18.358679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:19.782604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:21.238118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:22.700509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:24.407307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:30:56.730043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:30:57.975094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:30:59.272935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:00.440609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:01.688482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:02.927425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:04.075985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:05.469569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:07.225506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:08.611079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:10.725614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:13.740434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:15.122819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:16.733966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:18.432695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:19.980957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:21.306185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:22.768377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:24.598907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:30:56.786043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:30:58.029663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:30:59.325456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:00.506619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:01.742997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:02.981500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:04.136116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:05.541435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:07.291557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:08.671333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:11.007696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:13.806367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:15.185533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:16.825013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:18.505239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:20.046778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:21.369306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:22.829481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:24.766448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:30:56.848014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:30:58.090662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:30:59.384458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:00.566323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:01.801045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:03.038158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:04.201115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:05.619510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:07.359983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:08.737616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:11.249302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:13.896760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:15.248399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:16.910663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:18.579953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:20.116227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:21.434020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:22.893208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:24.934926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:30:56.909530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:30:58.149203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:30:59.444472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:00.624635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:01.859199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:03.094859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:04.259600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:05.690072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:07.458566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:08.799624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:11.519422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:13.962609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:15.390222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:17.001513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:18.651641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:20.184591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:21.524921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:22.958996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:25.123401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:30:56.974794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:30:58.215493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:30:59.507540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:00.685645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:01.918838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:03.158650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:04.318960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:05.788452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:07.550529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:08.859252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:11.716503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:14.030902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:15.463026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:17.105375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:18.719647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:20.252276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:21.619446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:23.022017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:25.310808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:30:57.040003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:30:58.278062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:30:59.566356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:00.743148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:01.973609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:03.217071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:04.376004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:05.856025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:07.629941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:09.000810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:11.864550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-09-16T15:30:59.004379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:00.248011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:01.496901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:02.632354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:03.891335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:05.271637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:06.995682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:08.404588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:10.289982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:13.454415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:14.928181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:16.449667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:18.221569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:19.618605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:21.097888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:22.565689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:31:23.805624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-09-16T15:31:32.503037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
IDAgeAneu_neckAneu_widthAneu_depthAneu_heightAneu_volumeVERcoil_length1coil_size1coil_length2coil_size2coil_length3coil_size3coil_length4coil_size4coil_length5coil_size5coil_countSexAneu_locationAdj_techIs_blebAneu_width_label
ID1.000-0.0270.020-0.171-0.197-0.264-0.2000.294-0.044-0.157-0.081-0.069-0.151-0.090-0.196-0.124-0.218-0.161-0.1980.0000.0380.4110.1810.143
Age-0.0271.0000.1010.1120.1210.1090.110-0.1060.0300.0380.0690.0420.0590.0380.0460.0370.0460.0300.1130.1300.0880.0960.0000.078
Aneu_neck0.0200.1011.0000.6720.5910.4370.579-0.1090.5430.5140.5610.5420.5560.5410.5550.5360.5100.4500.5990.0100.1020.1970.0330.457
Aneu_width-0.1710.1120.6721.0000.9050.7350.880-0.1070.7540.7840.7930.7800.7890.7600.7980.7500.7650.7000.7700.0000.0400.0840.0080.669
Aneu_depth-0.1970.1210.5910.9051.0000.8970.987-0.1330.7760.8170.8130.8040.8090.7730.8130.7600.7890.7300.7810.0510.0340.0530.0920.695
Aneu_height-0.2640.1090.4370.7350.8971.0000.848-0.1540.7110.7620.7390.7300.7360.7020.7360.6820.7060.6700.7020.0610.0000.0470.0780.524
Aneu_volume-0.2000.1100.5790.8800.9870.8481.000-0.1310.7310.7600.7580.7500.7490.7220.7610.7110.7390.6850.7440.0250.0000.0220.0000.214
VER0.294-0.106-0.109-0.107-0.133-0.154-0.1311.0000.0810.0560.0290.011-0.006-0.004-0.050-0.058-0.091-0.0900.0720.0000.0600.1350.0250.081
coil_length1-0.0440.0300.5430.7540.7760.7110.7310.0811.0000.8820.7900.7800.7590.7320.7180.6770.6610.6100.6030.0170.1020.1230.0380.557
coil_size1-0.1570.0380.5140.7840.8170.7620.7600.0560.8821.0000.8420.8580.8230.7940.7790.7330.7320.6790.6520.0200.0550.0740.0000.577
coil_length2-0.0810.0690.5610.7930.8130.7390.7580.0290.7900.8421.0000.9020.8610.8010.8060.7530.7420.6850.6460.0270.0700.0980.0000.587
coil_size2-0.0690.0420.5420.7800.8040.7300.7500.0110.7800.8580.9021.0000.8540.8570.7930.7810.7380.7090.6440.0000.0690.0890.0000.545
coil_length3-0.1510.0590.5560.7890.8090.7360.749-0.0060.7590.8230.8610.8541.0000.8900.8720.7940.7940.7240.6660.0540.0760.0850.0000.591
coil_size3-0.0900.0380.5410.7600.7730.7020.722-0.0040.7320.7940.8010.8570.8901.0000.8190.8480.7570.7450.6470.0000.0410.0780.0000.506
coil_length4-0.1960.0460.5550.7980.8130.7360.761-0.0500.7180.7790.8060.7930.8720.8191.0000.8630.8810.7860.6990.0530.0720.0440.0000.583
coil_size4-0.1240.0370.5360.7500.7600.6820.711-0.0580.6770.7330.7530.7810.7940.8480.8631.0000.7970.8450.6640.0000.0000.0120.0000.487
coil_length5-0.2180.0460.5100.7650.7890.7060.739-0.0910.6610.7320.7420.7380.7940.7570.8810.7971.0000.8430.6760.0550.0730.0310.0000.525
coil_size5-0.1610.0300.4500.7000.7300.6700.685-0.0900.6100.6790.6850.7090.7240.7450.7860.8450.8431.0000.6290.0000.0000.0000.0000.452
coil_count-0.1980.1130.5990.7700.7810.7020.7440.0720.6030.6520.6460.6440.6660.6470.6990.6640.6760.6291.0000.0560.0750.0720.0850.548
Sex0.0000.1300.0100.0000.0510.0610.0250.0000.0170.0200.0270.0000.0540.0000.0530.0000.0550.0000.0561.0000.3000.0720.0000.000
Aneu_location0.0380.0880.1020.0400.0340.0000.0000.0600.1020.0550.0700.0690.0760.0410.0720.0000.0730.0000.0750.3001.0000.1740.1340.101
Adj_tech0.4110.0960.1970.0840.0530.0470.0220.1350.1230.0740.0980.0890.0850.0780.0440.0120.0310.0000.0720.0720.1741.0000.0870.076
Is_bleb0.1810.0000.0330.0080.0920.0780.0000.0250.0380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0850.0000.1340.0871.0000.000
Aneu_width_label0.1430.0780.4570.6690.6950.5240.2140.0810.5570.5770.5870.5450.5910.5060.5830.4870.5250.4520.5480.0000.1010.0760.0001.000

Missing values

2023-09-16T15:31:27.498029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-16T15:31:28.050379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-16T15:31:28.479333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDSexAgeAneu_locationAneu_neckAneu_widthAneu_depthAneu_heightAneu_volumeAdj_techIs_blebVERcoil_length1coil_size1coil_length2coil_size2coil_length3coil_size3coil_length4coil_size4coil_length5coil_size5coil_countAneu_width_label
01woman69ICA3.86.95.955.0107.427250NaNnoNaN15.06.06.03.06.03.06.03.04.03.081.0
12woman49ICA2.78.29.5510.9446.706343NaNnoNaN30.08.020.06.020.05.010.04.010.04.092.0
23woman54ICA6.06.56.005.5112.255000NaNnoNaN15.07.010.05.010.04.08.04.0NaNNaN41.0
38woman63ICA5.011.011.0011.0696.556667NaNnoNaN30.010.030.08.020.08.015.06.015.06.0152.0
411woman58MCA4.47.97.707.5238.757750NaNnoNaN12.07.020.06.020.08.08.04.015.05.0122.0
516woman78ICA5.011.011.0011.0696.556667SimplenoNaN30.010.030.010.020.08.020.06.012.05.0152.0
617woman69MCA2.43.33.253.217.960800NaNnoNaN2.02.51.02.0NaNNaNNaNNaNNaNNaN20.0
720woman61ICA1.32.83.554.322.368313NaNnoNaN6.03.03.02.0NaNNaNNaNNaNNaNNaN20.0
827woman64ACA1.85.04.253.538.922917SimplenoNaN6.04.02.02.02.02.0NaNNaNNaNNaN31.0
930woman55ICA8.49.89.108.4392.035280SimplenoNaN30.010.08.04.08.04.08.04.010.03.0112.0
IDSexAgeAneu_locationAneu_neckAneu_widthAneu_depthAneu_heightAneu_volumeAdj_techIs_blebVERcoil_length1coil_size1coil_length2coil_size2coil_length3coil_size3coil_length4coil_size4coil_length5coil_size5coil_countAneu_width_label
16212503woman47ICA8.07.77.47.0208.736733Stent assistno26.50000030.08.015.05.015.04.015.04.06.03.052.0
16222504woman57ICA5.06.25.85.5103.504867Stent assistno24.70000012.06.010.05.08.04.03.02.02.01.061.0
16232506woman54ACA3.93.73.53.322.364650Stent assistyes29.9000008.03.52.52.02.01.52.01.5NaNNaN40.0
16242509woman45ICA5.04.54.54.446.629000Stent assistno22.9742477.04.010.04.04.03.04.02.03.01.560.0
16252510woman78ICA4.54.95.66.390.469680Stent assistno23.00000010.05.08.04.06.03.56.03.04.52.080.0
16262511woman46ICA4.24.74.33.941.248610Stent assistno24.1000008.04.08.03.54.53.02.52.0NaNNaN40.0
16272512woman58MCA2.62.7NaN3.90.000000NaNnoNaN4.03.03.02.02.02.0NaNNaNNaNNaN30.0
16282513woman50ICANaNNaNNaN4.00.000000NaNno25.0000006.04.04.03.03.02.02.02.0NaNNaN4NaN
16292514woman87ICA5.36.56.05.5112.255000Stent assistno27.20000015.05.08.04.08.03.56.03.63.52.5101.0
16302515man54VA5.88.17.77.3238.274190Stent assistno24.00000020.07.010.06.010.05.03.52.52.01.5162.0